Robotic Nanoparticle Synthesis via Solution-based Processes

TL;DR

This paper introduces a screw geometry-based robotic framework for automating complex solution-based nanoparticle synthesis, enabling flexible, demonstration-driven programming for chemists.

Contribution

It presents a novel screw-theoretic motion planning method combined with demonstration-based skill extraction for flexible, long-horizon laboratory automation.

Findings

Robots can autonomously perform multi-step nanoparticle synthesis tasks.

The method generalizes across variations in laboratory setups and grasp points.

Demonstration-driven screw extraction simplifies programming for chemists.

Abstract

We present a screw geometry-based manipulation planning framework for the robotic automation of solution-based synthesis, exemplified through the preparation of gold and magnetite nanoparticles. The synthesis protocols are inherently long-horizon, multi-step tasks, requiring skills such as pick-and-place, pouring, turning a knob, and periodic visual inspection to detect reaction completion. A central challenge is that some skills, notably pouring, transferring containers with solutions, and turning a knob, impose geometric and kinematic constraints on the end-effector motion. To address this, we use a programming by demonstration paradigm where the constraints can be extracted from a single demonstration. This combination of screw-based motion representation and demonstration-driven specification enables domain experts, such as chemists, to readily adapt and reprogram the system for new experimental protocols and laboratory setups without requiring expertise in robotics or motion planning. We extract sequences of constant screws from demonstrations, which compactly encode the motion constraints while remaining coordinate-invariant. This representation enables robust generalization across variations in grasp placement and allows parameterized reuse of a skill learned from a single example. By composing these screw-parameterized primitives according to the synthesis protocol, the robot autonomously generates motion plans that execute the complete experiment over repeated runs. Our results highlight that screw-theoretic planning, combined with programming by demonstration, provides a rigorous and generalizable foundation for long-horizon laboratory automation, thereby enabling fundamental kinematics to have a translational impact on the use of robots in developing scalable solution-based synthesis protocols.